bench_test-single_service.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "efe506b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle as pkl\n",
    "import numpy as np\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "507c25f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>front-end_ctn_latency</th>\n",
       "      <th>front-end_ctn_cpu</th>\n",
       "      <th>front-end_ctn_mem</th>\n",
       "      <th>front-end_ctn_net_in</th>\n",
       "      <th>front-end_ctn_net_out</th>\n",
       "      <th>user_ctn_latency</th>\n",
       "      <th>user_ctn_cpu</th>\n",
       "      <th>user_ctn_write</th>\n",
       "      <th>user_ctn_net_in</th>\n",
       "      <th>user_ctn_net_out</th>\n",
       "      <th>...</th>\n",
       "      <th>carts_ctn_net_out</th>\n",
       "      <th>payment_ctn_latency</th>\n",
       "      <th>payment_ctn_cpu</th>\n",
       "      <th>payment_ctn_net_in</th>\n",
       "      <th>payment_ctn_net_out</th>\n",
       "      <th>shipping_ctn_latency</th>\n",
       "      <th>shipping_ctn_cpu</th>\n",
       "      <th>shipping_ctn_mem</th>\n",
       "      <th>shipping_ctn_net_in</th>\n",
       "      <th>shipping_ctn_net_out</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.657720</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.058093</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.471765</td>\n",
       "      <td>0.981079</td>\n",
       "      <td>0.978965</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.162099</td>\n",
       "      <td>0.994471</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.008548</td>\n",
       "      <td>0.492663</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.911122</td>\n",
       "      <td>0.882570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.017990</td>\n",
       "      <td>0.985028</td>\n",
       "      <td>0.375233</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.563044</td>\n",
       "      <td>0.488073</td>\n",
       "      <td>0.981079</td>\n",
       "      <td>0.978965</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.183405</td>\n",
       "      <td>0.704644</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.020066</td>\n",
       "      <td>0.996092</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.985225</td>\n",
       "      <td>0.962042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.073682</td>\n",
       "      <td>0.973441</td>\n",
       "      <td>0.413766</td>\n",
       "      <td>0.480414</td>\n",
       "      <td>0.450550</td>\n",
       "      <td>0.057654</td>\n",
       "      <td>0.930022</td>\n",
       "      <td>0.416125</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.950522</td>\n",
       "      <td>0.175658</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.447896</td>\n",
       "      <td>0.406754</td>\n",
       "      <td>0.032583</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.985225</td>\n",
       "      <td>0.962042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.150836</td>\n",
       "      <td>0.973441</td>\n",
       "      <td>0.413766</td>\n",
       "      <td>0.885999</td>\n",
       "      <td>0.926857</td>\n",
       "      <td>0.191534</td>\n",
       "      <td>0.920819</td>\n",
       "      <td>0.416125</td>\n",
       "      <td>0.902045</td>\n",
       "      <td>0.903071</td>\n",
       "      <td>...</td>\n",
       "      <td>0.936012</td>\n",
       "      <td>0.368639</td>\n",
       "      <td>0.675846</td>\n",
       "      <td>0.894850</td>\n",
       "      <td>0.922096</td>\n",
       "      <td>0.009584</td>\n",
       "      <td>0.978790</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.185732</td>\n",
       "      <td>0.543334</td>\n",
       "      <td>0.201701</td>\n",
       "      <td>0.885999</td>\n",
       "      <td>0.926857</td>\n",
       "      <td>0.229331</td>\n",
       "      <td>0.844941</td>\n",
       "      <td>0.546673</td>\n",
       "      <td>0.902045</td>\n",
       "      <td>0.903071</td>\n",
       "      <td>...</td>\n",
       "      <td>0.936012</td>\n",
       "      <td>0.313498</td>\n",
       "      <td>0.904023</td>\n",
       "      <td>0.894850</td>\n",
       "      <td>0.922096</td>\n",
       "      <td>0.050059</td>\n",
       "      <td>0.946844</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.976205</td>\n",
       "      <td>0.992572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>0.887052</td>\n",
       "      <td>0.408775</td>\n",
       "      <td>0.512623</td>\n",
       "      <td>0.596795</td>\n",
       "      <td>0.658348</td>\n",
       "      <td>0.811795</td>\n",
       "      <td>0.544889</td>\n",
       "      <td>0.128072</td>\n",
       "      <td>0.596669</td>\n",
       "      <td>0.591773</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693733</td>\n",
       "      <td>0.865909</td>\n",
       "      <td>0.501476</td>\n",
       "      <td>0.654761</td>\n",
       "      <td>0.704219</td>\n",
       "      <td>0.869769</td>\n",
       "      <td>0.575908</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.585688</td>\n",
       "      <td>0.653356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>0.886897</td>\n",
       "      <td>0.764848</td>\n",
       "      <td>0.558597</td>\n",
       "      <td>0.596795</td>\n",
       "      <td>0.658348</td>\n",
       "      <td>0.822269</td>\n",
       "      <td>0.551085</td>\n",
       "      <td>0.140369</td>\n",
       "      <td>0.590627</td>\n",
       "      <td>0.585801</td>\n",
       "      <td>...</td>\n",
       "      <td>0.686480</td>\n",
       "      <td>0.907498</td>\n",
       "      <td>0.837438</td>\n",
       "      <td>0.645266</td>\n",
       "      <td>0.707000</td>\n",
       "      <td>0.875454</td>\n",
       "      <td>0.534167</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.585688</td>\n",
       "      <td>0.653356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>0.934587</td>\n",
       "      <td>0.758906</td>\n",
       "      <td>0.555408</td>\n",
       "      <td>0.601459</td>\n",
       "      <td>0.644896</td>\n",
       "      <td>0.770532</td>\n",
       "      <td>0.547518</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.590627</td>\n",
       "      <td>0.585801</td>\n",
       "      <td>...</td>\n",
       "      <td>0.686480</td>\n",
       "      <td>0.801288</td>\n",
       "      <td>0.837438</td>\n",
       "      <td>0.642210</td>\n",
       "      <td>0.706142</td>\n",
       "      <td>0.882662</td>\n",
       "      <td>0.521614</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.585688</td>\n",
       "      <td>0.653356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>0.932751</td>\n",
       "      <td>0.747571</td>\n",
       "      <td>0.373638</td>\n",
       "      <td>0.599936</td>\n",
       "      <td>0.651227</td>\n",
       "      <td>0.773720</td>\n",
       "      <td>0.232998</td>\n",
       "      <td>0.358322</td>\n",
       "      <td>0.598951</td>\n",
       "      <td>0.592316</td>\n",
       "      <td>...</td>\n",
       "      <td>0.692168</td>\n",
       "      <td>0.759345</td>\n",
       "      <td>0.495194</td>\n",
       "      <td>0.642210</td>\n",
       "      <td>0.706142</td>\n",
       "      <td>0.102727</td>\n",
       "      <td>0.497797</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.099849</td>\n",
       "      <td>0.141847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>0.967642</td>\n",
       "      <td>0.751471</td>\n",
       "      <td>0.373638</td>\n",
       "      <td>0.599936</td>\n",
       "      <td>0.651227</td>\n",
       "      <td>0.792103</td>\n",
       "      <td>0.564639</td>\n",
       "      <td>0.358322</td>\n",
       "      <td>0.594062</td>\n",
       "      <td>0.589109</td>\n",
       "      <td>...</td>\n",
       "      <td>0.697181</td>\n",
       "      <td>0.610201</td>\n",
       "      <td>0.753037</td>\n",
       "      <td>0.638780</td>\n",
       "      <td>0.713030</td>\n",
       "      <td>0.056345</td>\n",
       "      <td>0.497797</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.561893</td>\n",
       "      <td>0.625952</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    front-end_ctn_latency  front-end_ctn_cpu  front-end_ctn_mem  \\\n",
       "0                0.000000           1.000000           0.657720   \n",
       "1                0.017990           0.985028           0.375233   \n",
       "2                0.073682           0.973441           0.413766   \n",
       "3                0.150836           0.973441           0.413766   \n",
       "4                0.185732           0.543334           0.201701   \n",
       "..                    ...                ...                ...   \n",
       "56               0.887052           0.408775           0.512623   \n",
       "57               0.886897           0.764848           0.558597   \n",
       "58               0.934587           0.758906           0.555408   \n",
       "59               0.932751           0.747571           0.373638   \n",
       "60               0.967642           0.751471           0.373638   \n",
       "\n",
       "    front-end_ctn_net_in  front-end_ctn_net_out  user_ctn_latency  \\\n",
       "0               1.000000               1.000000          0.058093   \n",
       "1               1.000000               1.000000          0.000000   \n",
       "2               0.480414               0.450550          0.057654   \n",
       "3               0.885999               0.926857          0.191534   \n",
       "4               0.885999               0.926857          0.229331   \n",
       "..                   ...                    ...               ...   \n",
       "56              0.596795               0.658348          0.811795   \n",
       "57              0.596795               0.658348          0.822269   \n",
       "58              0.601459               0.644896          0.770532   \n",
       "59              0.599936               0.651227          0.773720   \n",
       "60              0.599936               0.651227          0.792103   \n",
       "\n",
       "    user_ctn_cpu  user_ctn_write  user_ctn_net_in  user_ctn_net_out  ...  \\\n",
       "0       1.000000        0.471765         0.981079          0.978965  ...   \n",
       "1       0.563044        0.488073         0.981079          0.978965  ...   \n",
       "2       0.930022        0.416125         1.000000          1.000000  ...   \n",
       "3       0.920819        0.416125         0.902045          0.903071  ...   \n",
       "4       0.844941        0.546673         0.902045          0.903071  ...   \n",
       "..           ...             ...              ...               ...  ...   \n",
       "56      0.544889        0.128072         0.596669          0.591773  ...   \n",
       "57      0.551085        0.140369         0.590627          0.585801  ...   \n",
       "58      0.547518        0.000000         0.590627          0.585801  ...   \n",
       "59      0.232998        0.358322         0.598951          0.592316  ...   \n",
       "60      0.564639        0.358322         0.594062          0.589109  ...   \n",
       "\n",
       "    carts_ctn_net_out  payment_ctn_latency  payment_ctn_cpu  \\\n",
       "0            1.000000             0.162099         0.994471   \n",
       "1            1.000000             0.183405         0.704644   \n",
       "2            0.950522             0.175658         1.000000   \n",
       "3            0.936012             0.368639         0.675846   \n",
       "4            0.936012             0.313498         0.904023   \n",
       "..                ...                  ...              ...   \n",
       "56           0.693733             0.865909         0.501476   \n",
       "57           0.686480             0.907498         0.837438   \n",
       "58           0.686480             0.801288         0.837438   \n",
       "59           0.692168             0.759345         0.495194   \n",
       "60           0.697181             0.610201         0.753037   \n",
       "\n",
       "    payment_ctn_net_in  payment_ctn_net_out  shipping_ctn_latency  \\\n",
       "0             1.000000             1.000000              0.008548   \n",
       "1             1.000000             1.000000              0.020066   \n",
       "2             0.447896             0.406754              0.032583   \n",
       "3             0.894850             0.922096              0.009584   \n",
       "4             0.894850             0.922096              0.050059   \n",
       "..                 ...                  ...                   ...   \n",
       "56            0.654761             0.704219              0.869769   \n",
       "57            0.645266             0.707000              0.875454   \n",
       "58            0.642210             0.706142              0.882662   \n",
       "59            0.642210             0.706142              0.102727   \n",
       "60            0.638780             0.713030              0.056345   \n",
       "\n",
       "    shipping_ctn_cpu  shipping_ctn_mem  shipping_ctn_net_in  \\\n",
       "0           0.492663               0.0             0.911122   \n",
       "1           0.996092               0.0             0.985225   \n",
       "2           1.000000               0.0             0.985225   \n",
       "3           0.978790               0.0             1.000000   \n",
       "4           0.946844               0.0             0.976205   \n",
       "..               ...               ...                  ...   \n",
       "56          0.575908               1.0             0.585688   \n",
       "57          0.534167               1.0             0.585688   \n",
       "58          0.521614               1.0             0.585688   \n",
       "59          0.497797               1.0             0.099849   \n",
       "60          0.497797               1.0             0.561893   \n",
       "\n",
       "    shipping_ctn_net_out  \n",
       "0               0.882570  \n",
       "1               0.962042  \n",
       "2               0.962042  \n",
       "3               1.000000  \n",
       "4               0.992572  \n",
       "..                   ...  \n",
       "56              0.653356  \n",
       "57              0.653356  \n",
       "58              0.653356  \n",
       "59              0.141847  \n",
       "60              0.625952  \n",
       "\n",
       "[61 rows x 34 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#f = open('collected_data_all_cpu.pkl', 'rb')\n",
    "names = ['front-end', 'user', 'catalogue', 'orders', 'carts', 'payment', 'shipping']\n",
    "metrics = ['ctn_latency', 'ctn_cpu', 'ctn_mem', 'ctn_write', 'ctn_read', 'ctn_net_in', 'ctn_net_out']\n",
    "#metrics = ['ctn_cpu', 'ctn_mem', 'ctn_write', 'ctn_read', 'ctn_net_in', 'ctn_net_out']\n",
    "idx = 3\n",
    "f = open('./data_collected/memory-leak1_'+names[idx]+'.pkl', 'rb')\n",
    "data = pkl.load(f)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "7ccca6da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['front-end_ctn_latency', 'front-end_ctn_cpu', 'front-end_ctn_mem',\n",
       "       'front-end_ctn_net_in', 'front-end_ctn_net_out', 'user_ctn_latency',\n",
       "       'user_ctn_cpu', 'user_ctn_write', 'user_ctn_net_in', 'user_ctn_net_out',\n",
       "       'catalogue_ctn_latency', 'catalogue_ctn_cpu', 'catalogue_ctn_net_in',\n",
       "       'catalogue_ctn_net_out', 'orders_ctn_latency', 'orders_ctn_cpu',\n",
       "       'orders_ctn_mem', 'orders_ctn_write', 'orders_ctn_net_in',\n",
       "       'orders_ctn_net_out', 'carts_ctn_latency', 'carts_ctn_cpu',\n",
       "       'carts_ctn_write', 'carts_ctn_net_in', 'carts_ctn_net_out',\n",
       "       'payment_ctn_latency', 'payment_ctn_cpu', 'payment_ctn_net_in',\n",
       "       'payment_ctn_net_out', 'shipping_ctn_latency', 'shipping_ctn_cpu',\n",
       "       'shipping_ctn_mem', 'shipping_ctn_net_in', 'shipping_ctn_net_out'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "1bde066b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "orders_ctn_read\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>orders_ctn_latency</th>\n",
       "      <th>orders_ctn_cpu</th>\n",
       "      <th>orders_ctn_mem</th>\n",
       "      <th>orders_ctn_write</th>\n",
       "      <th>orders_ctn_net_in</th>\n",
       "      <th>orders_ctn_net_out</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000915</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.633480</td>\n",
       "      <td>0.991138</td>\n",
       "      <td>0.981462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.001462</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000135</td>\n",
       "      <td>0.691352</td>\n",
       "      <td>0.991234</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.024797</td>\n",
       "      <td>0.048078</td>\n",
       "      <td>0.445068</td>\n",
       "      <td>0.691352</td>\n",
       "      <td>0.991234</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.117606</td>\n",
       "      <td>0.052988</td>\n",
       "      <td>0.445068</td>\n",
       "      <td>0.566713</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.994625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.155707</td>\n",
       "      <td>0.204833</td>\n",
       "      <td>0.889951</td>\n",
       "      <td>0.535831</td>\n",
       "      <td>0.844273</td>\n",
       "      <td>0.850420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>0.947948</td>\n",
       "      <td>0.999702</td>\n",
       "      <td>0.998308</td>\n",
       "      <td>0.135291</td>\n",
       "      <td>0.548645</td>\n",
       "      <td>0.530635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>0.942711</td>\n",
       "      <td>0.998842</td>\n",
       "      <td>0.998917</td>\n",
       "      <td>0.759383</td>\n",
       "      <td>0.057124</td>\n",
       "      <td>0.028838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>0.971075</td>\n",
       "      <td>0.998845</td>\n",
       "      <td>0.999848</td>\n",
       "      <td>0.759383</td>\n",
       "      <td>0.527630</td>\n",
       "      <td>0.519995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>0.991221</td>\n",
       "      <td>0.916368</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.759383</td>\n",
       "      <td>0.527630</td>\n",
       "      <td>0.519995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>0.984638</td>\n",
       "      <td>0.999225</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.852859</td>\n",
       "      <td>0.527630</td>\n",
       "      <td>0.519995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    orders_ctn_latency  orders_ctn_cpu  orders_ctn_mem  orders_ctn_write  \\\n",
       "0             0.000000        0.000915        0.000000          0.633480   \n",
       "1             0.001462        0.000000        0.000135          0.691352   \n",
       "2             0.024797        0.048078        0.445068          0.691352   \n",
       "3             0.117606        0.052988        0.445068          0.566713   \n",
       "4             0.155707        0.204833        0.889951          0.535831   \n",
       "..                 ...             ...             ...               ...   \n",
       "56            0.947948        0.999702        0.998308          0.135291   \n",
       "57            0.942711        0.998842        0.998917          0.759383   \n",
       "58            0.971075        0.998845        0.999848          0.759383   \n",
       "59            0.991221        0.916368        1.000000          0.759383   \n",
       "60            0.984638        0.999225        1.000000          0.852859   \n",
       "\n",
       "    orders_ctn_net_in  orders_ctn_net_out  \n",
       "0            0.991138            0.981462  \n",
       "1            0.991234            1.000000  \n",
       "2            0.991234            1.000000  \n",
       "3            1.000000            0.994625  \n",
       "4            0.844273            0.850420  \n",
       "..                ...                 ...  \n",
       "56           0.548645            0.530635  \n",
       "57           0.057124            0.028838  \n",
       "58           0.527630            0.519995  \n",
       "59           0.527630            0.519995  \n",
       "60           0.527630            0.519995  \n",
       "\n",
       "[61 rows x 6 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name=[]\n",
    "for i in metrics:\n",
    "    n = names[idx]+'_'+i\n",
    "    try:\n",
    "        if len(data[[n]]) != 0 :\n",
    "            name.append(n)\n",
    "    except:\n",
    "        print(n)        \n",
    "service_metric = data[name]\n",
    "service_metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "0c778a4a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 936x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "m = list(service_metric.columns)\n",
    "plt.figure(figsize=(13,10))\n",
    "for each in m:\n",
    "    plt.plot(service_metric[each], label=str(each))\n",
    "plt.legend(bbox_to_anchor=(1.5,0.5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3109,
   "id": "43d87979",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = service_metric"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc83e367",
   "metadata": {},
   "source": [
    "## PC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3110,
   "id": "13e27d6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6eb96c5ae43545558395dba9976645a8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PC result\n",
      "[[ 0  0 -1  0  0]\n",
      " [ 0  0  0  0  0]\n",
      " [-1  0  0  0  0]\n",
      " [ 0  0  0  0 -1]\n",
      " [ 0  0  0 -1  0]]\n",
      "[0, 2, 3, 4]\n",
      "[[0. 1. 0. 0.]\n",
      " [1. 0. 0. 0.]\n",
      " [0. 0. 0. 1.]\n",
      " [0. 0. 1. 0.]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from causallearn.search.ConstraintBased.PC import pc\n",
    "from causallearn.utils.cit import chisq, fisherz, gsq, kci, mv_fisherz\n",
    "\n",
    "cg = pc(X.to_numpy(), 0.05, fisherz, False, 0, -1)\n",
    "adj = cg.G.graph\n",
    "\n",
    "print('PC result')\n",
    "print(adj)\n",
    "\n",
    "# Change the adj to graph\n",
    "G = nx.DiGraph()\n",
    "for i in range(len(adj)):\n",
    "    for j in range(len(adj)):\n",
    "        if adj[i,j] == -1:\n",
    "            G.add_edge(i,j)\n",
    "        if adj[i,j] == 1:\n",
    "            G.add_edge(j,i)\n",
    "nodes = sorted(G.nodes())\n",
    "print(nodes)\n",
    "adj = np.asarray(nx.to_numpy_matrix(G, nodelist=nodes))\n",
    "print(adj)\n",
    "nx.draw(G, with_labels=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3111,
   "id": "00caaa72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.25 0.25 0.25 0.25]\n",
      "[(0, 0.25), (1, 0.25), (2, 0.25), (3, 0.25)]\n"
     ]
    }
   ],
   "source": [
    "# PageRank\n",
    "from sknetwork.ranking import PageRank\n",
    "pagerank = PageRank()\n",
    "scores = pagerank.fit_transform(adj.T)\n",
    "print(scores)\n",
    "#cmap = plt.cm.coolwarm\n",
    "\n",
    "score_dict = {}\n",
    "for i,s in enumerate(scores):\n",
    "    score_dict[i] = s\n",
    "print(sorted(score_dict.items(), key=lambda item:item[1], reverse=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "543e4e2a",
   "metadata": {},
   "source": [
    "## GES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3112,
   "id": "aa29591f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x0\n",
      "x1\n",
      "x2\n",
      "x3\n",
      "x4\n",
      "[[ 0  0  0  0  0]\n",
      " [ 0  0  0  0  0]\n",
      " [ 0  0  0  0  0]\n",
      " [ 0  0  0  0  1]\n",
      " [ 0  0  0 -1  0]]\n",
      "x0\n",
      "x1\n",
      "x2\n",
      "x3\n",
      "x4\n",
      "x3\n",
      "[[ 0  0  0  0  0]\n",
      " [ 0  0  0  0 -1]\n",
      " [ 0  0  0  0  0]\n",
      " [ 0  0  0  0 -1]\n",
      " [ 0  1  0 -1  0]]\n",
      "x0\n",
      "x1\n",
      "x2\n",
      "x3\n",
      "x4\n",
      "x3\n",
      "[[ 0  0  0 -1  0]\n",
      " [ 0  0  0  0 -1]\n",
      " [ 0  0  0  0  0]\n",
      " [ 1  0  0  0 -1]\n",
      " [ 0 -1  0 -1  0]]\n",
      "x0\n",
      "x1\n",
      "x2\n",
      "x3\n",
      "x4\n",
      "x1\n",
      "[[ 0  0  0 -1  0]\n",
      " [ 0  0  0  0 -1]\n",
      " [ 0  0  0  0  1]\n",
      " [-1  0  0  0 -1]\n",
      " [ 0 -1 -1 -1  0]]\n",
      "x0\n",
      "x1\n",
      "x2\n",
      "x3\n",
      "x4\n",
      "x0\n",
      "x1\n",
      "x2\n",
      "x3\n",
      "x0\n",
      "[[ 0  0  0 -1  1]\n",
      " [ 0  0  0  0 -1]\n",
      " [ 0  0  0  0 -1]\n",
      " [-1  0  0  0 -1]\n",
      " [-1 -1 -1 -1  0]]\n",
      "x0\n",
      "x1\n",
      "x2\n",
      "x3\n",
      "x4\n",
      "x0\n",
      "x1\n",
      "x2\n",
      "x3\n",
      "x0\n",
      "[[ 0  1  0 -1 -1]\n",
      " [-1  0  0  0 -1]\n",
      " [ 0  0  0  0 -1]\n",
      " [-1  0  0  0 -1]\n",
      " [-1 -1 -1 -1  0]]\n",
      "x0\n",
      "x1\n",
      "x2\n",
      "x3\n",
      "x4\n",
      "x0\n",
      "x2\n",
      "x3\n",
      "x4\n",
      "x2\n",
      "x3\n",
      "backward\n",
      "[[ 0 -1  0 -1  0]\n",
      " [ 1  0  0  0  1]\n",
      " [ 0  0  0  0 -1]\n",
      " [-1  0  0  0 -1]\n",
      " [ 0 -1 -1 -1  0]]\n",
      "x0\n",
      "x1\n",
      "x2\n",
      "x3\n",
      "x4\n",
      "x0\n",
      "x1\n",
      "x2\n",
      "[[ 0  0  0 -1  0]\n",
      " [ 0  0  0  0  1]\n",
      " [ 0  0  0  0 -1]\n",
      " [-1  0  0  0 -1]\n",
      " [ 0 -1 -1 -1  0]]\n",
      "x0\n",
      "x1\n",
      "x2\n",
      "x3\n",
      "x4\n",
      "x0\n",
      "x1\n",
      "x2\n",
      "x3\n",
      "x0\n",
      "[0, 1, 2, 3, 4]\n",
      "[[0. 0. 0. 1. 0.]\n",
      " [0. 0. 0. 0. 1.]\n",
      " [0. 0. 0. 0. 1.]\n",
      " [1. 0. 0. 0. 1.]\n",
      " [0. 1. 1. 1. 0.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from causallearn.search.ScoreBased.GES import ges\n",
    "\n",
    "#X = data.iloc[:,1:]\n",
    "X = X - np.tile(np.mean(X, axis=0), (X.shape[0], 1))\n",
    "X = np.dot(X, np.diag(1 / np.std(X, axis=0)))\n",
    "maxP = 5  # maximum number of parents when searching the graph\n",
    "parameters = {'kfold': 10, 'lambda': 0.01}\n",
    "#X = X[:50, :]\n",
    "Record = ges(X, 'local_score_CV_general', maxP=maxP, parameters=parameters)\n",
    "adj = Record['G'].graph\n",
    "\n",
    "# Change the adj to graph \n",
    "G = nx.DiGraph()\n",
    "for i in range(len(adj)):\n",
    "    for j in range(len(adj)):\n",
    "        if adj[i,j] == -1:\n",
    "            G.add_edge(i,j)\n",
    "        if adj[i,j] == 1:\n",
    "            G.add_edge(j,i)\n",
    "nodes = sorted(G.nodes())\n",
    "print(nodes)\n",
    "adj = np.asarray(nx.to_numpy_matrix(G, nodelist=nodes))\n",
    "print(adj)\n",
    "nx.draw(G, with_labels=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3113,
   "id": "fb9e826e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.12988314 0.13586787 0.13586787 0.25446353 0.34391759]\n",
      "[(4, 0.3439175925336885), (3, 0.25446353356271056), (1, 0.13586786801267792), (2, 0.13586786801267792), (0, 0.12988313787824513)]\n"
     ]
    }
   ],
   "source": [
    "# PageRank\n",
    "from sknetwork.ranking import PageRank\n",
    "pagerank = PageRank()\n",
    "scores = pagerank.fit_transform(adj.T)\n",
    "print(scores)\n",
    "#cmap = plt.cm.coolwarm\n",
    "\n",
    "score_dict = {}\n",
    "for i,s in enumerate(scores):\n",
    "    score_dict[i] = s\n",
    "print(sorted(score_dict.items(), key=lambda item:item[1], reverse=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "347e5beb",
   "metadata": {},
   "source": [
    "## ICA-LINGAM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3114,
   "id": "fcfcd018",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LiNGAM result\n",
      "[3, 1, 4, 0, 2]\n",
      "[[ 0.          0.         -0.74358624  0.          0.        ]\n",
      " [-0.46071425  0.         -0.62678378  0.          0.        ]\n",
      " [ 0.          0.          0.          0.          0.        ]\n",
      " [-0.47618622  0.99450923 -0.92301354  0.          0.99990184]\n",
      " [ 0.          0.          0.          0.          0.        ]]\n"
     ]
    }
   ],
   "source": [
    "from causallearn.search.FCMBased import lingam\n",
    "#model = lingam.DirectLiNGAM()\n",
    "model = lingam.ICALiNGAM()\n",
    "model.fit(X)\n",
    "\n",
    "print('LiNGAM result')\n",
    "print(model.causal_order_) # the later virable is unable to cause the former virable\n",
    "#print(model.adjacency_matrix_)\n",
    "\n",
    "# Acutal matrix: row points to col \n",
    "adj = model.adjacency_matrix_.T \n",
    "# For PageRank, should adj.T\n",
    "print(adj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0922c115",
   "metadata": {},
   "outputs": [],
   "source": [
    "org_G = nx.from_numpy_matrix(adj, parallel_edges=True, create_using=nx.DiGraph)\n",
    "pos=nx.circular_layout(org_G)\n",
    "nx.draw(org_G, pos=pos, with_labels=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50c77eff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# PageRank\n",
    "from sknetwork.ranking import PageRank\n",
    "pagerank = PageRank()\n",
    "scores = pagerank.fit_transform(np.abs(adj.T))\n",
    "print(scores)\n",
    "#cmap = plt.cm.coolwarm\n",
    "\n",
    "score_dict = {}\n",
    "for i,s in enumerate(scores):\n",
    "    score_dict[i] = s\n",
    "print(sorted(score_dict.items(), key=lambda item:item[1], reverse=True))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}